Project Overview
This comprehensive machine learning project showcases advanced capabilities in both traditional ML and deep learning through two production-ready applications. Developed as part of CST3133 - Advanced Topics in Data Science and Artificial Intelligence, this platform demonstrates expertise in handling real-world data challenges while achieving exceptional performance metrics.
Developed collaboratively by a team of 4 students, combining expertise in data science, machine learning, and software engineering to create a comprehensive analytics solution.
Dual-Model Architecture
Student Performance Predictor
Advanced Random Forest model analyzing 15+ student factors to predict academic performance with exceptional accuracy. Handles severely corrupted educational data through custom preprocessing algorithms.
Key Features:
- Multi-output prediction capability
- Feature importance analysis
- Handles missing and corrupted data
- Real-time prediction interface
Fake News Detector
State-of-the-art LSTM neural network leveraging GloVe embeddings to identify misinformation with near-perfect accuracy. Processes text data through advanced NLP pipelines for reliable classification.
Key Features:
- LSTM with attention mechanism
- GloVe embeddings integration
- GPU-optimized training
- Production-ready API
Technical Implementation Pipeline
End-to-End Machine Learning Workflow
Data Collection
Kaggle datasets with real-world noise and corruption
Data Cleaning
Custom algorithms for 15+ corruption types
EDA & Analysis
Statistical insights and visualization
Feature Engineering
Domain-specific transformations
Model Training
Hyperparameter optimization
Deployment
Streamlit interactive demo
Overcoming Real-World Data Challenges
The Corrupted Data Challenge
The educational dataset presented unique challenges with over 15 different types of data corruption, including mixed encodings, special characters, missing values, and format inconsistencies. Our team developed sophisticated preprocessing algorithms that not only cleaned the data but preserved meaningful patterns, enabling the model to achieve its exceptional 98% R² score.
Data Preprocessing Innovations
# Custom preprocessing pipeline handling multiple corruption types
def advanced_data_cleaner(df):
# Handle mixed encodings
df = fix_encoding_issues(df)
# Smart imputation based on feature correlations
df = intelligent_imputation(df)
# Remove anomalies while preserving edge cases
df = adaptive_outlier_removal(df)
return df
Technology Stack & Architecture
Core Technologies
- Python 3.8+
- TensorFlow 2.x
- Keras Neural Networks
- scikit-learn
- Pandas & NumPy
- NLTK for NLP
- Matplotlib & Seaborn
- Streamlit
ML/DL Techniques
- Random Forest Ensemble
- LSTM Networks
- GloVe Embeddings
- Cross-Validation
- GridSearch Optimization
- Feature Importance Analysis
- Confusion Matrix Analysis
- ROC-AUC Evaluation
Data Science
- Exploratory Data Analysis
- Statistical Testing
- Correlation Analysis
- Data Visualization
- Feature Engineering
- Anomaly Detection
- Missing Data Imputation
- Performance Metrics
Infrastructure
- GPU Acceleration (CUDA)
- Model Versioning
- Docker Containerization
- GitHub CI/CD
- Streamlit Deployment
- REST API Design
- Cloud-Ready Architecture
- Performance Monitoring
Algorithm Selection & Performance
Model Comparison Analysis
| Model | Task | Accuracy | Training Time | Inference Speed | Interpretability |
|---|---|---|---|---|---|
| Random Forest | Student Performance | 99.73% | <1 minute | ~10ms | High (feature importance) |
| LSTM + GloVe | Fake News Detection | 99.99% | 18 seconds (GPU) | ~50ms | Medium (attention weights) |
| Baseline Models | Both Tasks | ~85-90% | Varies | Fast | High |
Results & Real-World Impact
Educational Impact
Enables early intervention for at-risk students through accurate performance prediction
Media Integrity
Protects users from misinformation with near-perfect fake news detection
Performance
Production-ready models with millisecond inference times
Accessibility
User-friendly Streamlit interface for non-technical stakeholders
Key Achievements
- Developed custom data cleaning algorithms handling 15+ corruption types
- Achieved state-of-the-art performance: 98% R² for regression, 99.99% for classification
- Created modular, production-ready code with comprehensive documentation
- Built interactive demo showcasing real-time predictions
- Demonstrated expertise across ML paradigms - from ensemble methods to deep learning
- Collaborated effectively in a team of 4 to deliver a comprehensive solution
Skills Demonstrated & Learning Outcomes
Machine Learning Mastery
- Traditional ML (Random Forest, XGBoost)
- Deep Learning (LSTM, Embeddings)
- Hyperparameter optimization
- Model evaluation and selection
- Ensemble methods
Software Engineering
- Clean, modular code architecture
- Comprehensive documentation
- Version control with Git
- Collaborative development
- API design principles
Data Science Excellence
- Advanced EDA techniques
- Feature engineering
- Statistical analysis
- Data visualization
- Performance optimization
Deployment & Production
- Streamlit application development
- Model serving strategies
- Performance monitoring
- User interface design
- Cloud deployment readiness
Future Development Roadmap
API Development
Deploy models as RESTful APIs with authentication, rate limiting, and comprehensive documentation for enterprise integration.
Real-time Processing
Implement streaming data pipelines for continuous model updates and real-time prediction capabilities.
Mobile Application
Develop cross-platform mobile apps for on-the-go access to predictions and analytics dashboards.
AutoML Integration
Incorporate automated machine learning for continuous model improvement and adaptation to new data patterns.
Explainable AI
Add SHAP/LIME interpretability tools for transparent decision-making and regulatory compliance.
Explore the Code
Dive into the implementation details and see how we achieved these exceptional results